Active contours with a joint and region‐scalable distribution metric for interactive natural image segmentation. Issue 12 (1st December 2014)

• Record Type: Journal Article
• Authors: Liu, Xin; Peng, Shu‐Juan; Cheung, Yiu‐ming; Tang, Yuan Yan; Du, Ji‐Xiang
• Is Part Of: IET image processing. Volume 8:Issue 12(2014)
• Page Start: 824
• Page End: 832
• Publication Date: 2014-12-01
• Subjects: image segmentation -- independent component analysis -- image colour analysis -- probability -- filtering theory; active contour -- joint distribution metric -- region‐scalable distribution metric -- interactive natural image segmentation -- red–green–blue image -- CIELab colour space -- independent component analysis -- subspace channels -- evolving curve -- polygonal curve -- joint probability distribution -- region‐scalable mask -- regional statistics -- resultant level set function -- binary pattern -- circular averaging filter; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2013.0594
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In this study, we present an efficient active contour with a joint and region‐scalable distribution metric for interactive natural image segmentation. First, the authors project a red–green–blue image into the CIELab colour space and employ independent component analysis to select two subspace channels. Then, by initialising the evolving curve interactively in terms of a polygonal curve or multiple polygonal curves, they compute a joint probability distribution associated with a region‐scalable mask to model the regional statistics and propose a simple but effective distribution metric to regularise the active contours. Subsequently, they convert the resultant level set function into binary pattern and find the larger 8‐connected regions as the desired objects. Finally, the selected regions are smoothed with a circular averaging filter such that the final segmentation results can be obtained. The proposed approach not only can deal with the complex appearance and intensity in homogeneity, but also has the advantages of fast convergence and easy implementation. The experiments have shown the precise and reliable segmentation results in comparison with the state‐of‐the‐art competing approaches.